webiks-hebrew-ragbot 1.3.0

A search engine using machine learning models and Elasticsearch for advanced document retrieval.

Webiks-Hebrew-RAGbot

Overview

This project is a search engine that uses machine learning models and Elasticsearch to provide advanced document retrieval. You can use Webiks-Hebrew-RAGbot-Demo to demonstrate the engine's document retrieval abilities

Features

Document representation and validation Document embedding and indexing in Elasticsearch Advanced search using machine learning model Integration with LLM (Large Language Model) client for query answering

Installation

1. Clone the repository:

git clone https://github.com/NNLP-IL/Webiks-Hebrew-RAGbot.git

cd Webiks-Hebrew-RAGbot

2. Create a virtual environment and activate it:  

python -m venv venv

source venv/bin/activate

On Windows use \venv\\Scripts\\activate\

3. Install the required dependencies:  

pip install -r requirements.txt

Configuration

Set the following environment variables:  

MODEL_LOCATION: Path to the model directory ES_EMBEDDING_INDEX_LENGTH: Size of any index in elasticsearch EMBEDDING_INDEX: The name of the index we will embed docs into